ReviewJournal of applied clinical medical physics2023
Feature selection methods and predictive models in CT lung cancer radiomics.
Review in Journal of applied clinical medical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
36 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Pooled it
- Mastering CT-based radiomic research in lung cancer: a practical guide from study design to critical appraisal.The British journal of radiology · 2025Guideline
- Artificial Intelligence computed tomography models for the discrimination of Wilms versus non-Wilms tumors: systematic review and meta-analysis.Jornal brasileiro de nefrologiaPooled it
- Machine Learning-Based Prediction of Delayed Cerebral Ischemia and Functional Outcome in Aneurysmal Subarachnoid Hemorrhage: The Role of CNS Infection-Related Parameters.Biomedicines · 2026Article
- Impact of Radiomics Parameters and Clinical Integration on Prognostication in Head and Neck Squamous Cell Carcinoma: A Multicenter Study.Life (Basel, Switzerland) · 2026Article
- Computed Tomography-Based Assessment of Sarcopenia and Disease Progression in Pancreatic Ductal Adenocarcinoma: A Radiomics and Machine Learning Approach.Gastroenterology research · 2026Article
- Interpretable MRI Radiomics for Preoperative Meningioma Consistency Prediction.Journal of imaging informatics in medicine · 2026Article
- Application of SHAP visualization and nomogram based on PET/CT radiomics combined models for risk stratification in high-grade invasive lung adenocarcinoma.Journal of translational medicine · 2026Article
- Explainable deep learning and radiomics integration for differentiating insulinomas from NF-PNETs in EUS imaging.BMC medical informatics and decision making · 2026Article
- Data-Driven Analysis of Systemic Indicators Linking Stroke-Associated Pneumonia, Delayed Cerebral Ischemia, and Outcome After Aneurysmal Subarachnoid Hemorrhage.Journal of clinical medicine · 2026Article
- Evaluating consistency of radiomic features derived from CT images: A cross-center phantom study.Journal of applied clinical medical physics · 2026Article
- Prognostic potential of radiomics evaluation of lung artery thrombus for pulmonary embolism patients.International journal of computer assisted radiology and surgery · 2026Article
- Radiomics in spinal research: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Computed Tomography-Based Radiomics Prediction of Biochemical Failure and Distant Metastasis in Patients With High- and Very High-Risk Localized Prostate Cancer.Advances in radiation oncology · 2025Article
- Evaluating feature extraction reproducibility across image biomarker standardization initiative-compliant radiomics platforms using a digital phantom.Journal of applied clinical medical physics · 2025Article
- Radiomics: Assessing Significance and Correlation with Ground-Truth Data in Precision Medicine in Lung Adenocarcinoma.Bioengineering (Basel, Switzerland) · 2025Article
- Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction.NPJ digital medicine · 2025Article
- Detecting severe coronary artery stenosis in T2DM patients with NAFLD using cardiac fat radiomics-based machine learning.Scientific reports · 2025Article
- An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer.Scientific reports · 2025Article
- Predictive value of CT radiomics and inflammatory markers for pulmonary adenocarcinoma spread through air spaces.American journal of cancer research · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiomics is a technique that extracts quantitative features from medical images using data-characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.